6 accepted papers
Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing attention due to significant performance improvements over foundation models. While single-stage post-training such as reinfo…
Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively. The key challenges lie in in-depth content generation, appropriate and expressive voice production. This paper proposed PodAgent, a comprehensive framework for creating audio programs. PodAgent…
Style voice conversion aims to transform the speaking style of source speech into a desired style while keeping the original speaker’s identity. However, previous style voice conversion approaches primarily focus on well-defined domains such as emotional aspects, limiting their practical application…
FastSpeech, as a feed-forward transformer based TTS, can avoid the slow serial, autoregressive inference to generate the target mel-spectrogram in a parallel way. As a non-autoregressive TTS, the latency and computation load in inference is shifted from vocoder to transformer where the efficiency is…
This paper proposes ProsodySpeech, a novel prosody model to enhance encoder-decoder neural Text-To-Speech (TTS), to generate high expressive and personalized speech even with very limited training data. First, we use a Prosody Extractor built from a large speech corpus with various speakers to gener…
Recent advances of neural TTS have made "human parity" synthesized speech possible when a large amount of studio-quality training data from a voice talent is available. However, with only limited, casual recordings from an ordinary speaker, human-like TTS is still a big challenge, in addition to oth…